MLA-C01 Practice Test 41 — 15 Questions

Question 1

A company uses SageMaker Clarify to detect bias during training. They want to ensure that the trained model does not rely on a sensitive attribute like gender. Which Clarify feature should they configure?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Clarify bias config with post-training bias metrics

Answer

Clarify with SageMaker Model Monitor

Answer

SHAP analysis

Answer

Clarify processing job with pre-training bias metrics

Answer

Bias report generation

Question 2

A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

Accepted Answer

Use Retrieval-Augmented Generation (RAG) with the policy documents indexed in a vector store. RAG allows the LLM to retrieve relevant document sections at inference time, so knowledge stays current without retraining.

Answer

Use a larger foundation model with a longer context window and paste all documents into each prompt

Answer

Fine-tune a base LLM on the policy documents monthly

Answer

Train a custom model from scratch on the policy documents each month

Question 3

A team is training a large model on SageMaker using the SageMaker distributed training library with model parallelism. They need to choose the most cost-effective instance type. Which instance family offers the best balance of performance and cost for large model training?

Accepted Answer

ml.trn1

Answer

ml.g4dn

Answer

ml.p3

Answer

ml.c5

Question 4

A data scientist is using SageMaker Experiments to track multiple training runs. They want to compare different hyperparameter configurations and visualize the impact on model accuracy. What should they use to track hyperparameters?

Accepted Answer

SageMaker Experiments. SageMaker Experiments allows you to log hyperparameters as parameters. They can be viewed and compared across runs in the SageMaker Studio UI.

Answer

SageMaker Debugger

Answer

SageMaker Autopilot

Answer

SageMaker Model Monitor

Question 5

A fraud detection model is being trained on imbalanced data. The team wants to ensure the model's precision is optimized. Which objective metric should be used in automatic model tuning?

Accepted Answer

Precision

Answer

F1

Answer

AUC

Answer

Recall

Question 6

A company wants to use SageMaker Clarify to analyze bias in their training data and model predictions. Which TWO types of bias can Clarify detect? (Choose TWO.)

Accepted Answer

Pre-training bias. SageMaker Clarify can detect pre-training bias (in the data) and post-training bias (in the model predictions).

Answer

Algorithmic bias

Answer

Inference bias

Answer

Deployment bias

Question 7

A data scientist is using SageMaker built-in Image Classification algorithm on a dataset with 1000 classes. The training is very slow. They want to speed it up without sacrificing accuracy. Which instance type and training configuration is MOST appropriate?

Accepted Answer

Use ml.p3.16xlarge instances with data parallelism. For image classification, GPU instances like ml.p3 or ml.g4dn are suitable. ml.p3.16xlarge provides 8 V100 GPUs. ml.m5 is CPU only. ml.c5 is CPU. ml.trn1 is for training, but for this built-in algorithm, GPU instances are standard.

Answer

Use ml.trn1.32xlarge instances with data parallelism

Answer

Use ml.m5.24xlarge instances with data parallelism

Answer

Use ml.c5.18xlarge instances with model parallelism

Question 8

Which SageMaker built-in algorithm should be used for forecasting time series data with seasonal patterns?

Accepted Answer

DeepAR. DeepAR is a supervised learning algorithm for time series forecasting that handles seasonality and trends.

Answer

IP Insights

Answer

BlazingText

Answer

Factorization Machines

Question 9

Refer to the exhibit. A SageMaker Processing job configured as above fails with a timeout error. The input data is 100 GB of CSV files. The processing script performs standard data cleaning operations. What is the most likely cause?

Accepted Answer

The processing job does not have enough memory for the data volume. The SageMaker Processing job is configured with a single `ml.m5.large` instance, which has 8 GiB of memory. The input data is 100 GB of CSV files, and the processing script performs standard data cleaning operations that typically load the entire dataset into memory (e.g., using pandas). With only 8 GiB of RAM, the instance cannot hold 100 GB of data, causing the job to run out of memory and eventually fail with a timeout error as the OS kills the process or the job hangs.

Answer

The container entrypoint is missing the full path to the script

Answer

The S3Input S3CompressionType is set to "None" but the file is compressed

Answer

The IAM role does not have permission to write to the output bucket

Question 10

A company is using AWS Glue to prepare data for a machine learning pipeline. The source data is in an Amazon S3 bucket in CSV format. The data scientist wants to convert the data to Parquet format and partition it by date. Which AWS Glue feature should be used to optimize the data for query performance and reduce storage costs?

Accepted Answer

Use AWS Glue DynamicFrame to repartition the data and write it as Parquet.. AWS Glue DynamicFrames provide built-in optimizations for writing data in columnar formats like Parquet, which improves query performance through predicate pushdown and compression, and reduces storage costs by using efficient encoding. The DynamicFrame's `repartition()` method allows you to control the number of output files, and writing as Parquet directly from Glue avoids intermediate conversions, making it the most efficient choice for this task.

Answer

Use Amazon Athena to convert the data to JSON format and store it in S3.

Answer

Use AWS Glue to convert the data to Apache Hive format.

Answer

Use Apache Spark DataFrame to write the data as CSV with Snappy compression.

Question 11

An ML team has deployed a model to a SageMaker real-time endpoint and wants to set up automated monitoring for model quality. Which TWO elements are required to configure SageMaker Model Monitor for model quality? (Select TWO.)

Accepted Answer

A ground truth labels dataset for comparison. SageMaker Model Monitor for model quality requires a ground truth labels dataset to compare the model's predictions against actual outcomes. This comparison is essential for calculating quality metrics like accuracy, precision, recall, or F1 score, which indicate how well the model is performing over time.

Answer

SHAP values for feature attribution

Answer

A constraints file with allowed deviation thresholds

Answer

A baseline statistics file derived from the training data

Question 12

A company is using SageMaker Automatic Model Tuning to optimize a regression model. They want to minimize the root mean squared error (RMSE). The tuner has completed 20 jobs, and the RMSE has plateaued. Which action should the data scientist take to potentially improve the results?

Accepted Answer

Decrease the range of hyperparameters to focus on promising areas. Reducing the search space can help the tuner focus on more promising regions. Increasing parallelism or max jobs may explore the same plateau, while switching to a different algorithm altogether might not be necessary.

Answer

Increase the maximum number of training jobs

Answer

Increase the number of parallel training jobs

Answer

Switch the objective metric to mean absolute error (MAE)

Question 13

A team uses SageMaker Model Monitor to track data quality. They notice that the monitor's constraint violations are increasing but the model performance remains good. What should they do?

Accepted Answer

Investigate the specific features that are violating constraints to see if they are still relevant.. Increasing constraint violations in SageMaker Model Monitor do not necessarily indicate model degradation; they may reflect benign data drift where feature distributions shift but the model's predictive performance remains intact. Investigating specific violating features allows the team to determine whether the drift is meaningful (e.g., due to a real-world change that the model should adapt to) or irrelevant (e.g., a feature that is no longer used in the inference pipeline). This aligns with the monitoring best practice of separating data quality alerts from model performance metrics to avoid unnecessary retraining or threshold tuning.

Answer

Disable the monitor because it is not affecting performance.

Answer

Relax the constraint thresholds to reduce alerts.

Answer

Retrain the model using the latest data.

Question 14

A company is training a large NLP model on SageMaker and wants to reduce costs by using Spot Instances. Which TWO configurations should they implement to handle Spot interruptions gracefully?

Accepted Answer

Set `use_spot_instances=True` and `max_wait` in the estimator. Checkpointing saves progress so training can resume from the last checkpoint. Managed Spot Training with `use_spot_instances=True` automates handling of interruptions. Using a single instance or increasing max runtime does not handle interruptions; `keep_alive_period` is for persistent notebooks, not training.

Answer

Use a single large instance to reduce interruption probability

Answer

Increase the `max_run` parameter to allow longer training

Answer

Use `keep_alive_period` to keep the instance alive after training

Question 15

A startup is building a serverless inference API using AWS Lambda. They have a TensorFlow model that is 400 MB in size. They packaged the model and inference code into a Lambda function using a container image. When they test the function with a small input, it consistently times out after 3 seconds. The Lambda function has 512 MB of memory and a timeout of 30 seconds. The business requirement is that inference must complete in less than 5 seconds under normal conditions. What is the most likely cause of the slow performance, and which change should they make?

Accepted Answer

The Lambda function memory is insufficient for the model size; increase memory to 1024 MB or higher.. The most likely cause is that the Lambda function's memory (512 MB) is insufficient to load the 400 MB TensorFlow model into memory, causing excessive swapping or out-of-memory errors that drastically slow inference. Increasing memory to 1024 MB or higher provides more CPU and memory resources, allowing the model to fit and inference to complete within the required 5 seconds.

Answer

The function timeout is too low; increase the timeout to 60 seconds.

Answer

The function is experiencing a cold start; use provisioned concurrency to keep the container warm.

Answer

Use a Lambda function with a GPU container to accelerate inference.